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NishMath - #machinelearning
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@mastodon.acm.org
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blog.siggraph.org
, forge.dyalog.com
Advancements in machine learning, APL programming, and computer graphics are driving innovation across various disciplines. ACM Transactions on Probabilistic Machine Learning (TOPML) is highlighting the importance of probabilistic machine learning with its recently launched journal, featuring high-quality research in the field. The journal's co-editors, Wray Buntine, Fang Liu, and Theodore Papamarkou, share their insights on the significance of probabilistic ML and the journal's mission to advance the field.
The APL Forge competition is encouraging developers to create innovative open-source libraries and commercial applications using Dyalog APL. This annual event aims to enhance awareness and usage of APL by challenging participants to solve problems and develop tools using the language. The competition awards £2,500 (GBP) and an expenses-paid trip to present at the next user meeting, making it a valuable opportunity for APL enthusiasts to showcase their skills and contribute to the community. The deadline for submissions is Monday 22 June 2026. SIGGRAPH 2025 will showcase advancements in 3D generative AI as part of its Technical Papers program. This year's program received a record number of submissions, highlighting the growing interest in artificial intelligence, large language models, robotics, and 3D modeling in VR. Professor Richard Zhang of Simon Fraser University has been inducted into the ACM SIGGRAPH Academy for his contributions to spectral and learning-based methods for geometric modeling and will be the SIGGRAPH 2025 Technical Papers Chair. Recommended read:
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Haden Pelletier@Towards Data Science
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Recent discussions in statistics highlight significant concepts and applications relevant to data science. A book review explores seminal ideas and controversies in the field, focusing on key papers and historical perspectives. The review mentions Fisher's 1922 paper, which is credited with creating modern mathematical statistics, and discusses debates around hypothesis testing and Bayesian analysis.
Stephen Senn's guest post addresses the concept of "relevant significance" in statistical testing, cautioning against misinterpreting statistical significance as proof of a genuine effect. Senn points out that rejecting a null hypothesis does not necessarily mean it is false, emphasizing the importance of careful interpretation of statistical results. Furthermore, aspiring data scientists are advised to familiarize themselves with essential statistical concepts for job interviews. These include understanding p-values, Z-scores, and outlier detection methods. A p-value is crucial for hypothesis testing, and Z-scores help identify data points that deviate significantly from the mean. These concepts form a foundation for analyzing data and drawing meaningful conclusions in data science applications. Recommended read:
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@www.marktechpost.com
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MIT researchers are making significant strides in artificial intelligence, focusing on enhancing AI's ability to learn and interact with the world more naturally. One project involves developing AI models that can learn connections between vision and sound without human intervention. This innovative approach aims to mimic how humans learn, by associating what they see with what they hear. The model could be useful in applications such as journalism and film production, where the model could help with curating multimodal content through automatic video and audio retrieval.
The new machine-learning model can pinpoint exactly where a particular sound occurs in a video clip, eliminating the need for manual labeling. By adjusting how the original model is trained, it learns a finer-grained correspondence between a particular video frame and the audio that occurs in that moment. The enhancements improved the model’s ability to retrieve videos based on an audio query and predict the class of an audio-visual scene, like the sound of a roller coaster in action or an airplane taking flight. Researchers also made architectural tweaks that help the system balance two distinct learning objectives, which improves performance. Additionally, researchers from the National University of Singapore have introduced 'Thinkless,' an adaptive framework designed to reduce unnecessary reasoning in language models. Thinkless reduces unnecessary reasoning by up to 90% using DeGRPO. By incorporating a novel algorithm called Decoupled Group Relative Policy Optimization (DeGRPO), Thinkless separates the training focus between selecting the reasoning mode and improving the accuracy of the generated response. This framework equips a language model with the ability to dynamically decide between using short or long-form reasoning, addressing the issue of resource-intensive and wasteful reasoning sequences for simple queries. Recommended read:
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Carl Franzen@AI News | VentureBeat
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Microsoft has announced the release of Phi-4-reasoning-plus, a new small, open-weight language model designed for advanced reasoning tasks. Building upon the architecture of the previously released Phi-4, this 14-billion parameter model integrates supervised fine-tuning and reinforcement learning to achieve strong performance on complex problems. According to Microsoft, the Phi-4 reasoning models outperform larger language models on several demanding benchmarks, despite their compact size. This new model pushes the limits of small AI, demonstrating that carefully curated data and training techniques can lead to impressive reasoning capabilities.
The Phi-4 reasoning family, consisting of Phi-4-reasoning, Phi-4-reasoning-plus, and Phi-4-mini-reasoning, is specifically trained to handle complex reasoning tasks in mathematics, scientific domains, and software-related problem solving. Phi-4-reasoning-plus, in particular, extends supervised fine-tuning with outcome-based reinforcement learning, which is targeted for improved performance in high-variance tasks such as competition-level mathematics. All models are designed to enable reasoning capabilities, especially on lower-performance hardware such as mobile devices. Microsoft CEO Satya Nadella revealed that AI is now contributing to 30% of Microsoft's code. The open weight models were released with transparent training details and evaluation logs, including benchmark design, and are hosted on Hugging Face for reproducibility and public access. The model has been released under a permissive MIT license, enabling its use for broad commercial and enterprise applications, and fine-tuning or distillation, without restriction. Recommended read:
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Adam Zewe@news.mit.edu
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MIT researchers have unveiled a "periodic table of machine learning," a groundbreaking framework that organizes over 20 common machine-learning algorithms based on a unifying algorithm. This innovative approach allows scientists to combine elements from different methods, potentially leading to improved algorithms or the creation of entirely new ones. The researchers believe this framework will significantly fuel further AI discovery and innovation by providing a structured approach to understanding and developing machine learning techniques.
The core concept behind this "periodic table" is that all these algorithms, while seemingly different, learn a specific kind of relationship between data points. Although the way each algorithm accomplishes this may vary, the fundamental mathematics underlying each approach remains consistent. By identifying a unifying equation, the researchers were able to reframe popular methods and arrange them into a table, categorizing each based on the relationships it learns. Shaden Alshammari, an MIT graduate student and lead author of the related paper, emphasizes that this is not just a metaphor, but a structured system for exploring machine learning. Just like the periodic table of chemical elements, this new framework contains empty spaces, representing algorithms that should exist but haven't been discovered yet. These spaces act as predictions, guiding researchers toward unexplored areas within machine learning. To illustrate the framework's potential, the researchers combined elements from two different algorithms, resulting in a new image-classification algorithm that outperformed current state-of-the-art approaches by 8 percent. The researchers hope that this "periodic table" will serve as a toolkit, allowing researchers to design new algorithms without needing to rediscover ideas from prior approaches. Recommended read:
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@www.analyticsvidhya.com
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OpenAI recently unveiled its groundbreaking o3 and o4-mini AI models, representing a significant leap in visual problem-solving and tool-using artificial intelligence. These models can manipulate and reason with images, integrating them directly into their problem-solving process. This unlocks a new class of problem-solving that blends visual and textual reasoning, allowing the AI to not just see an image, but to "think with it." The models can also autonomously utilize various tools within ChatGPT, such as web search, code execution, file analysis, and image generation, all within a single task flow.
These models are designed to improve coding capabilities, and the GPT-4.1 series includes GPT-4.1, GPT-4.1 mini, and GPT-4.1 nano. GPT-4.1 demonstrates enhanced performance and lower prices, achieving a 54.6% score on SWE-bench Verified, a significant 21.4 percentage point increase from GPT-4o. This is a big gain in practical software engineering capabilities. Most notably, GPT-4.1 offers up to one million tokens of input context, compared to GPT-4o's 128k tokens, making it suitable for processing large codebases and extensive documentation. GPT-4.1 mini and nano also offer performance boosts at reduced latency and cost. The new models are available to ChatGPT Plus, Pro, and Team users, with Enterprise and education users gaining access soon. While reasoning alone isn't a silver bullet, it reliably improves model accuracy and problem-solving capabilities on challenging tasks. With Deep Research products and o3/o4-mini, AI-assisted search-based research is now effective. Recommended read:
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